Method of controlling traffic in a cellular network and system thereof

ABSTRACT

There is provided a method and system to control traffic in a cellular network comprising a plurality of access points (APs) serving a plurality of user equipment devices (UEs). The method comprises: continuously obtaining data informative of NW KPIs derived from network data related to at least part of APs; continuously obtaining data informative of one or more UE KPIs derived from user equipment data related to at least part of the UEs; and processing the one or more NW KPIs together with the one or more UE KPIs to identify AP(s) requiring corrective action. The method can further comprise enabling, with regard to the identified AP(s), desirable corrective actions to improve the UE KPIs related to the identified AP(s) whilst to keep the NW KPIs related to the identified AP(s) as matching one or more predefined thresholds.

TECHNICAL FIELD

The presently disclosed subject matter relates generally to systems andmethods of cellular communication and, in particular, to systems andmethods of controlling quality of performance in a cellular network.

BACKGROUND

For today's complex cellular networks, Self-Organizing Network (SON)capabilities have become essential in order to configure, organize,optimise performance and/or provide self-healing if/when faults occur.The major aspects of SON technology are self-configuration, self-healingand self-optimization. Self-configuration enables access points (e.g.macrocells, picocells, femtocells base stations, eNBs, etc. and/orgroups thereof) to become “Plug and Play” items. Among the aspectsaddressed by the self-healing capability are detection of celldegradation and respective self-recovery of software, self-healing; ofboard faults, cell outage recovery, and/or cell outage compensation,etc.

Once the system has been set up, operational characteristics of accesspoints (APs) can be tuned by corrective actions, based on analyses ofmeasurements data, thus enabling achievement of optimal networkperformance, coverage, and/or capacity by self-optimisation of thenetwork. Self-optimization functions can include, for example, LoadBalancing, Handover Optimization, Coverage & Capacity Optimization, CellOutage Compensation, Energy Saving Management, etc. These optimizationfunctions can change the coverage and capacity of a cell and, possibly,of surrounding cells, by configuring parameters (e.g. transmission powerfor downlink transmissions, antenna tilt, azimuth parameters, etc.) ofthe respective access point(s). Optionally, self-optimization functionscan be aggregated into use cases (e.g. mobility load balancing (MLB),coverage and capacity optimization (CCO), and mobility robustnessoptimization (MRO)), which may be independent or may interact since theycan operate on common control parameters.

Typically, corrective actions required for network optimization arebased on current and desired (and/or threshold) Key PerformanceIndicators (KPIs). KPIs can characterize accessibility, retainability,integrity, mobility, availability and/or other quality characteristicsas defined by respective cellular standards. A given KPI can be relatedto all communication services provided by a given AP, a group of APsand/or within a given location, or can be related to one or moreselected communication-related services. Unless specifically statedotherwise, it is appreciated that throughout the specification the term“KPI” should be expansively construed to cover various statisticalmetrics including, but not limited to success metrics (e.g. throughput,call drop, etc.) and objective metrics.

General Description

The inventors recognized that used in the art network-centricperformance KPIs are poorly correlated to the actual users' experience,and that there is a need to consider KPIs indicative of services asreceived by the users.

In accordance with certain aspect of the presently disclosed subjectmatter, there is provided a method of controlling traffic in a cellularnetwork comprising a plurality of access points (APs) serving aplurality of user equipment devices (UEs). The method comprises:continuously obtaining data informative of one or more network KeyPerformance Indicators (NW KPIs) derived from network data (NW data)related to at least part of the APs from the plurality of APs;continuously obtaining data informative of one or more user equipmentKey Performance Indicators (UE KPIs) derived from user equipment data(UE data) related to at least part of the UEs from the plurality of UEs;and processing the one or more NW KPIs together with the one or more UEKPIs to identify at least one AP requiring corrective action.

The method can further comprise at least on of: reporting the at leastone identified AP requiring corrective actions; identifying problemsrelated to the at least one identified AP and alerting thereof;identifying one or more desirable corrective actions with regard to theat least one identified AP; and enabling desirable corrective actionswith regard to the at least one identified AP.

The one or more NW KPIs and the one or more UE KPIs can be indicative ofdifferent characteristics related to network performance. Alternativelyor additionally, the one or more NW KPIs can be indicative of networkperformance parameters, and the one or more UE KPIs can be indicative ofexperience of respective users.

In accordance with further aspects, the method can further compriseenabling, with regard to the identified at least one AP, one or moredesirable corrective actions to improve the one or more UE KPIs relatedto the identified at least one AP whilst to keep the one or more NW KPIsrelated to the identified at least one AP as matching one or morepredefined thresholds.

NW KPIs can be derived from the NW data aggregated in statisticalclusters using a first aggregation criterion, and the UE KPIs can beseparately derived from the UE data aggregated in statistical clustersusing a second aggregation criterion different from the firstaggregation criterion, the method can further comprise correlatingbetween NW KPIs and UE KPIs prior to the processing the respective KPIs.The correlating can comprise identifying NW KPIs that correspond to APsinvolved in one or more services characterized by the UE KPIs.Optionally, the second aggregation criterion can be related to UEslocated in one or more predefined geographical areas (e.g. correspondingto a highway or a railway) and/or related to UEs moving with a speedexceeding a predefined threshold.

In accordance with other aspect of the presently disclosed subjectmatter, there is provided a method of controlling traffic in a cellularnetwork comprising a plurality of access points (APs) serving aplurality of user equipment devices (UEs), the method comprising:continuously obtaining network data (NW data) related to at least partof APs from the plurality of APs; continuously obtaining user equipmentdata (UE data) related to at least part of UEs from the plurality ofUEs; processing the obtained NW data together with UE data to obtain oneor more enhanced KPIs; and using the obtained one or more enhanced KPIsto identify at least one AP requiring corrective action.

The method can further comprise at least on of: reporting the at leastone identified AP requiring corrective actions; reporting the one ormore enhanced KPIs; identifying problems related to the at least oneidentified AP and alerting thereof; identifying one or more desirablecorrective actions with regard to the at least one identified AP; andenabling desirable corrective actions with regard to the at least oneidentified AP.

Optionally, prior to the processing, the NW data and the UE data can beaggregated in combined statistical clusters with different weights foraggregating NW data and UE data.

The method can further comprise: aggregating NW data in NW statisticalclusters and separately aggregating UE data in UE statistical clusters;and correlating between the NW statistical clusters and the UEstatistical clusters, wherein the processing comprises processingtogether the correlated NW statistical clusters and the UE statisticalclusters.

The processing can comprise: deriving one or more NW KPIs from the NWstatistical clusters; deriving one or more UE KPIs from the UEstatistical clusters; and deriving one or more enhanced KPIs, wherein atleast one enhanced KPI is configured as a single value representing aweighted combination of one or more NW KPIs and respectively correlatedone or more UE KPIs. A weight of the one or more UE KPIs in the at leastone enhanced KPI can depend on a use case related to desirablecorrective actions.

Alternatively or additionally, the processing can comprise: deriving oneor more NW KPIs from the NW statistical clusters; deriving one or moreUE KPIs from the UE statistical clusters; deriving one or more enhancedKPIs, wherein at least one enhanced KPI is configured as two values, oneof the two values corresponding to the one or more NW KPIs and anotherof the two values, to respectively correlated one or more UE KPIs.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method of controlling traffic in a cellularnetwork comprising a plurality of access points (APs) serving aplurality of user equipment devices (UEs), the method comprises:continuously obtaining one or more UE KPIs indicative of failedconnectivity attempts by at least part of the UEs from the plurality ofUEs; processing the one or more UE KPIs to identify one or more firstAPs corresponding to one or more UE KPIs indicative of failedconnectivity attempts exceeding a predefined threshold; and enablingcorrective actions to improve the one or more UE KPIS indicative offailed connectivity attempts and related to the one or more identifiedAPs. Optionally, at least part of the UE KPIs can be received by thecomputerized system from an external source.

Continuously obtaining the one or more UE KPIs can comprise:continuously collecting, by a computerized system, UE data related to atleast part of the UEs from the plurality of UEs; and continuouslyderiving, by the computerized system, the one or more UE KPIs from thecollected UE data. Optionally, the one or more UE KPIs can be derived byprocessing merely a part of the collected UE data selected in accordancewith at least one of the following criteria: data corresponding to apredefined geographical area; data corresponding to predefined APs; datacorresponding to geographical areas meeting predefined values ofsignal-strength indicative parameters; and data corresponding topredefined UEs, Optionally, at least part of the UE data can becollected with the help of active connectivity tests initiated from atleast part of the UEs.

In accordance with further aspects, the method can further comprise:identifying, among the one or more first APs corresponding to locationbins with UE KPI(s) indicative of failed connectivity attempts exceedinga predefined threshold, one or more second APs characterized by receivedsignal strength meeting a predefined criterion; and enabling correctiveactions to reduce downlink coverage of the identified location bins bythe identified one or more second APs. Optionally, the one or moresecond APs can be identified with the help of processing the one or moreUE KPIs together with NW KPIs corresponding to the one or more firstAps. Alternatively or additionally, the one or more second APs can beidentified with the help of UE data received from UEs configured tostore data indicative of received signal strength and report the storeddata responsive to a predefined event that triggers UE data reporting.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method of controlling traffic in a cellularnetwork comprising a plurality of access points (APs) serving aplurality of user equipment devices (UEs), the method comprises:continuously obtaining one or more first UE KPIs characterizingcommuting UEs with regard to one or more APs serving the commuting UEs;identifying at least one AP serving the commuting UEs and requiringcorrective actions in accordance with the obtained one or more first UEKPIs; and enabling one or more corrective actions to improve performanceof the at least one identified AP with regard to the commuting UEswhilst enabling that a performance averaged over all UEs served by theat least one identified AP meets a predefined criterion.

In accordance with further aspects, the method can further comprisecontinuously obtaining by the computerized system one or more NW KPIswith regard to the at least one identified AP, wherein performance ofthe at least one identified AP with regard to the commuting UEs ischaracterized by the one or more first UE performance averaged over allUEs served by the at least one identified AP is characterized byrespective NW KPIs, and wherein the one or more corrective actions areenabled to improve the respective one or more first UE KPIs related tothe at least one identified AP whilst to keep the respective NW KPIsrelated to the at least one identified AP as matching one or morepredefined thresholds.

The continuously obtaining the one or more first UE KPIs characterizingcommuting UEs can comprise: continuously collecting by the computerizedsystem UE data related to at least part of the UEs from the plurality ofUEs; using the collected data to identify the commuting UEs andcontinuously deriving the one or more first UE KPIs from the UE datacollected from the commuting UEs. At least part of UE data can becollected with the help of active performance tests initiated from atleast part of the UEs. At least part of the one or more first UE KPIscan be received by the computerized system from an external source.

In accordance with further aspects, the commuting UEs can be identifiedin accordance with at least one of the following criteria: UE locationderived by the computerized system from UE data reported by UE to thecomputerized system; combination of UE location and speed derived by thecomputerized system from UE data reported by UE to the computerizedsystem; and location and/or speed-related data of one or more 3rd partyapplications derived by the computerized system from UE data. reportedby UE to the computerized system.

In accordance with further aspects, performance of the at least oneidentified AP with regard to the commuting UEs can be characterized bythe one or more first UE KPIs, performance averaged over all UEs servedby the at least one identified AP can be characterized by one or moresecond UE KPIs derived from UE data received from UEs configured tostore and report data indicative of received signal strength; and theone or more corrective actions can be enabled to improve the respectiveone or more first UE KPIs related to the at least one identified APwhilst to keep the one or more second UE KPIs related to the at leastone identified AP as matching one or more predefined thresholds.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a computerized system configured to perform atleast one of the methods above.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a computer program product implemented on anon-transitory computer usable medium and comprising computer readableprogram code for performing at least one of the methods above.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIGS. 1a-1b illustrate exemplified generalized cellular networkenvironments including a SON system configured in accordance withcertain embodiments of the presently disclosed subject matter;

FIG. 2 illustrates a generalized block diagram of the SON system inaccordance with certain embodiments of the presently disclosed subjectmatter;

FIGS. 3a and 3b illustrate a schematic diagram representing the problemof “access black hole”;

FIG. 4 illustrates a generalized flow diagram of a non-limiting exampleof operating the SON system in accordance with certain embodiments ofthe presently disclosed subject matter in an “access black hole” usecase;

FIG. 5 illustrates a generalized flow diagram of a non-limiting exampleof operating the SON system in accordance with certain embodiments ofthe presently disclosed subject matter in a “road” use case;

FIG. 6 illustrates a generalized flow diagram of operating the SONsystem in accordance with certain embodiments of the presently disclosedsubject matter; and

FIG. 7 illustrates a generalized flow diagram of operating the SONsystem in accordance with another certain embodiments of the presentlydisclosed subject matter.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “enabling”,“reporting”, “alerting”, “generating”, “obtaining”, “identifying” or thelike, refer to the action(s) and/or process(es) of a computer thatmanipulate and/or transform data into other data, said data representedas physical, such as electronic, quantities and/or said datarepresenting the physical objects. The term “computer” should beexpansively construed to cover any kind of hardware-based electronicdevice with data processing capabilities including, by way ofnon-limiting example, the SON system disclosed in the presentapplication.

It is to be understood that the term “non-transitory” is used herein toexclude transitory, propagating signals, but to include, otherwise, anyvolatile or non-volatile computer memory technology suitable to thepresently disclosed subject matter.

The operations in accordance with the teachings herein may be performedby a computer specially constructed for the desired purposes or by ageneral-purpose computer specially configured for the desired purpose bya computer program stored in a computer readable storage medium.

Bearing this in mind, attention is drawn to FIGS. 1a and FIGS. 1billustrating non-limiting examples of cellular network environmentincluding a SON system 121 configured in accordance with certainembodiments of the presently disclosed subject matter. FIG. 1aillustrates a generalized 3G network environment, and FIG. 1billustrates a generalized environment of LTE or 5G cellular networks.The exemplified network environments illustrated in FIGS. 1a and 1bcomprise a radio access network (RAN) denoted 120-1 for 3G network inFIG. 1a and 120-2 for LTE network in FIG. 1b . RAN comprises a pluralityof access points (e.g. macrocells, picocells, femtocells base stations,eNBs, etc. and/or groups thereof) denoted as 103-1-103-4. The accesspoints can operate in different bands and/or RATs (Radio AccessTechnologies) and can be provided by different vendors. The accesspoints (APs) serve a multiplicity of user equipment devices (UEs) beingin operating or in idle modes. The UEs are configured to communicatewith APs via radio frequency (RF) channels allowing bidirectionaltransmission of voice and/or data between the APs and UEs in accordancewith respective cellular standards. It is noted that the tenor “userequipment devices” should be expansively construed to cover anyend-device (e.g. mobile phones, IoT devices, telematic units, etc.)having capability to communicate with APs in accordance with respectivecellular protocols

SON system 121 can be connected (by direct connection or through amediation layer as, for example, OSS server) to access points 103(connections not shown for simplicity of illustration). The SON systemcan be configured to enable corrective actions (e.g. self-configuring,self-optimizing and/or self-healing, etc.) with regard to one or moreaccess points in accordance with data related to the network environmentand obtained by the SON system. As will be further detailed withreference to FIG. 2, SON system 121 can comprise a Network Information(NI) Module 101 configured to collect and process data related to thenetwork environment. NI module 101 comprises a memory 104 operativelycoupled to a processor and memory circuitry (PMC) 105 configured toenable operations as detailed with reference to FIGS. 2-7. PMC 105comprises a processor (not shown separately) operatively coupled to amemory (not shown separately). Memory 104 is configured to accommodate adatabase configured to store the collected data and the results ofprocessing thereof.

Network Information (NI) module 101 is operatively connected to one ormore network depositories denoted as 102-1 and 102-2. Optionally, someof the network depositories can be provided by different vendors. Unlessspecifically stated otherwise, any depository of data received from theplurality of APs (directly, via probe devices and/or otherwise) isreferred to hereinafter as a network data (NI)) depository. By way ofnon-limiting example, in a 3G environment such depositories can belocated at Radio Network Controller(s) (RNC), OSS (Operation and SupportSystem) servers, etc. By way of another non-limiting example, in LTEenvironment such depositories can be located at OSS servers and/or atMME. The NI module (101) can be further operatively connected to aPerformance Management (PM) node (106) and, optionally, to a Policy andCharging Rules Function (PCRF) node (107), billing system (108) and/orother client-related system.

The NI module is configured to continuously collect RAN-related datafrom one or more ND depositories. RAN-related data can be informative ofnetwork performance (including resource utilization). By way ofnon-limiting example, RAN-related data can be informative of ReferenceSignal Received Power (RSRP), Reference Signal Received Quality (RSRQ),Received Signal Strength Indication (RSSI), Signal-Noise Ratio (SNR),Channel Quality Indicator (CQI), mobility-related KPIs such as HandoverSuccess Rate and Dropped Call Rate (DCR), performance KPIs such userthroughput, network accessibility KPIs, etc. Optionally, RAN-relateddata can be derived from various data records (e.g xDR, where x standsfor Call/Transaction/Session) collected from ND depositories andinformative of network performance.

Alternatively or additionally to receiving data collected in one or moreNI) depositories, the NI module can be configured to continuouslycollect RAN-related data from a plurality of probe devices. The probedevices are denoted as 109-1-109-4 in FIG. 1a (and are not shown in FIG.1b for simplicity of illustration). The probe devices are operativelyconnected to the respective access points to sniff and/or monitortraffic (e.g. between the access points and the MME). The NI module canreceive data directly from the probe devices (e.g. as illustrated forthe probe devices 109-3 and 109-4) or via a network depository (e.g. asillustrated for the probe devices 109-1 and 109-2). Optionally, the NImodule can continuously receive data collected by the probe devices innear real-time mode, independently of collection periods of the networkdepositories.

RAN-related data and/or derivatives thereof received from the NDdepositories and/or probe devices and/or directly from the plurality ofaccess points are referred to hereinafter as network (NW) data. It isnoted that RAN-related data are collected in association with dataindicative of respective APs. KPIs corresponding to NW data andrespective association thereof are referred to hereinafter as NW KPIs.Such KPis can be received by the NI module from network entities (e.g.PM node 106) and/or obtained by processing NW data collected by the NImodule.

NI module 101 can be configured to store the obtained NW data and,optionally, NW KPIs in the database accommodated on the memory 104.Optionally, the NI module can be further configured to receive and storein the database data informative of network topology, includinggeolocation and configuration of access points.

In accordance with certain embodiments of the presently disclosedsubject matter, SON system 121 (e.g. NI module therein) can be furtherconfigured to operatively communicate with a plurality of UEs (denotedas 111-1-111-3) and to continuously collect data therefrom. Theplurality of UEs can include some or all UEs of the multiplicity of UEsserved by the plurality of APs UE 111 can be configured to continuouslyreport (directly or via other network entities) to SON system 121 datainformative, at least, of RAN-related measurements provided by UE 111 inaccordance with respective cellular standards.

RAN-related measurements can be related to (and associated with) aserving AP and one or more surrounding APs (not necessarily included ina neighbouring list of the serving cell) and/or locations associatedthereof. For example, RAN-related measurements can include RRCmeasurements provided by UEs for RRC (Radio Resource Control)measurement reports in accordance with 3G or higher standards. By way ofnon-limiting example, data informative of RAN-related measurements caninclude RSRP, RSRQ, RSSI, CQI, data throughput, latency, packet loss,etc. Alternatively or additionally, RAN-related measurements can includemeasurements which are not included in RRC reporting (e.g. call-dropindications, reasons the calls were dropped, etc.)

In accordance with certain embodiments of the presently disclosedsubject matter, further to data informative of RAN-related measurements,UE 111 can be configured to continuously report, and SON system 121 canbe configured to continuously collect data informative of UE locationand, optionally, of other context-related data (and/or derivativesthereof) obtained using various sensors of the UE (e.g. gyroscope,accelerometer, GPS, etc. and/or applications running on UE. By way ofnon-limiting example, context-related data can include battery levelthreshold, battery charge rate of change, predicted location of UE basedon UE mobility and/or UE calendar information, calendar information,alarm information, application data from an application running on theUE, type of UE (e.g. smartphone, IoT item, etc.), name of mobileoperator, environment-related status e.g. indoor, outdoor, driving,walking, etc.), etc.

Optionally, UE 111 can be configured to process at least part of datainformative of RAN-related measurements and/or context-related data andto send the respective derivatives to NI module 101. By way ofnon-limiting example, UE 111 can be configured to reveal a violationand/or near violation of a SLA (Service Level Agreement) stored in theUE and to send the respective alert to NI module 101.

Unless specifically stated otherwise, it is appreciated that throughoutthe specification the terms “continuously collecting data by the SONsystem”, or the like refer to receiving (in push or pull mode) datasubstantially each time new data is available to the SON system. Forexample, in “push” mode, the availability of new data in the system canbe defined by a period specified as collection time and/or reportingtime for a given ND depository, UE, etc., by availability of connectionwith a given UE, by predefined “push” events (e.g. events specified inthe cellular network for providing RRC measurements, events of SLA.violations, events specified by NI module for UE reporting, etc.), etc.In “pull” mode, the availability of new data can be defined byconfiguration of the NI module specifying when to pull the data.Likewise, the terms “continuously obtaining” “continuously reporting”,“continuously generating”, “continuously providing” and the like, referto actions (of NI module, SON system and/or UE) provided in accordancewith a certain arrangement related to new data availability. Forexample, such actions can be provided in near real-time mode, responsiveto predefined events, etc. It is noted that, unless specifically statedotherwise, the term “predefined events” should be expansively construedto cover also scheduled events and/or events occurring in accordancewith predefined periodicity.

Throughout the specification the term “UE data” is referred to dataobtained from UEs (e.g. RAN-related data, context-related data, etc.)and/or derivatives thereof. UE data can be collected. by SON system fromthe plurality of UEs. Alternatively or additionally, UE data can bereceived by the SON system from other network entities (e.g. from anoperator's big data repositories) and/or external entities (e.g. fromapplications collecting geo-indicated data from UEs). It is noted thatUE data are collected in association with data indicative of respectiveAPs and, optionally, with geolocation of UEs (and/or other context) whenthey obtained and/or reported the respective data. KPIs corresponding toUE data and respective associations thereof are referred to hereinafteras UE KPIs. Such KPIs can be received from other entities and/orobtained by processing UE data by the SON system.

NI module 101 can be configured to store the obtained UE data and UEKPIs in the database accommodated on memory 104.

In accordance with certain embodiments of the presently disclosedsubject matter, each UE 111 from the plurality of UEs is configured torun a software agent (referred to hereinafter as UE agent). UE agent canbe configured to enable reporting UE data to SON system 121, wherein UEdata can be collected by the agent from respective sensors and/or datarepositories comprised in UE and/or applications running on UE.Optionally, UE agent can be further configured to cause UE to storethere within operational data obtained by OS (e.g. data indicative ofsignal strength, data indicative of running applications, etc.) and/orother context-related data for further reporting and/or analyses.Further, UE agent can be configured to cause UE to provide activenetwork-related measurements. By way of non-limiting example, UE agentcan cause UE to send and/or receive a test data and to measure (andfurther report) respective performance characteristics (e.g. packetloss, throughput, latency, etc.).

By way of non-limiting example, UE agent can be implemented as an SDK toa mobile OS (e.g. Android), thus enabling access to network and deviceperformance and context information obtained and/or collected by the UE;an application having access to the respective information and builtwith the help of SDK and/or API provided by a device manufacture or by3^(rd) party application; etc. Operational parameters (e.g. when andwhat data to obtain, collect and/or report) of UE agent can beconfigurable by SON system 121.

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not hound by the networkenvironments illustrated in FIGS. 1a and 1b , and can be implemented inother network architectures and/or standards.

Referring to FIG. 2, there is illustrated a generalized block diagram ofSON system 121 in accordance with certain embodiments of the presentlydisclosed subject matter. The SON system comprises NI module 101operatively coupled to interface circuitry 201 (e.g. one or more ports),interface circuitry 202, output interface circuitry 203 and provisioningmodule 207. The SON system can further comprise a graphical userinterface (not shown) enabling user-specified inputs related to itsoperation.

Interface circuitry 201 is configured to enable bidirectional datacommunication necessary for continuously receiving NW data and/or NWKPIs, and interface circuitry 202 is configured to enable bidirectionaldata communication necessary for continuously receiving UE data and/orUE KPIs. Interface circuitries 201 and 202 are further configured toforward the received data and/or derivatives thereof to NI module 101which comprises PMC 105 and memory 104 operatively coupled to the PMC.PMC 105 is configured to provide necessary processing of the receivedand/or stored data in accordance with the operations further detailedwith reference to FIGS. 3-7. PMC 105 is further configured toaccommodate all thresholds, criteria and predefined rules, policies,training sets and alike detailed with reference to FIGS. 3-7. It isnoted that, optionally, PMC 105 can be shared with and/or incorporatedin other modules in the SON system.

Provisioning module 207 is configured to generate provisioning scripts(and/or select pre-provisioned scripts) in accordance with the resultsof NI module's processing and enable respective corrective actions (e.g.using OSS NBI and/or other network-provided. APIs/NBIs, includingchanges therein). Output interface circuitry 203 is configured toforward the respective provisional scripts to relevant network entities.

PMC 105 can be configured to execute several functional modules inaccordance with computer-readable instructions implemented on anon-transitory computer usable medium therein. Such functional modulesare referred to hereinafter as comprised in the PMC.

In accordance with certain embodiments of the presently disclosedsubject matter, PMC 105 can comprise such operatively connectedfunctional modules as UE management module 205 and data analyser 206.

UE management module 205 is configured to communicate (directly or viaother network entities) with the plurality of UEs 111 via interfacecircuitry 202. In accordance with certain embodiments of the invention,NI module UE management module 205) can communicate with each UE agentrunning on a given UE of the plurality of UEs.

UE management module 205 is configured to maintain one or more policiesspecifying parameters of data communication between NI module and theplurality of UEs. By way of non-limiting example, such parameters candefine (optionally in dependency, at least, on time and/or UE location)UE data to be measured and/or reported time window and/or location forUE data collection and/or reporting; predefined events triggering UEdata reporting, etc. The policies can be edited by a user (a person viaGUI or an external application via API). UE management module can beconfigured to download to each given UE from the plurality of UEs arespective policy and to provide periodic updates of the agent and/ordownloaded policies. A given UE (with the help of UE agent runningthereon) provides UE data to NI module 101 in accordance with therespectively received policy. It is noted that NI module 101 can providedifferent policies to different UEs.

As will be further detailed with reference to FIGS. 2-7, data analyser206 can be configured to process the collected NW and/or UE data toderive, in consideration of UE data, KPIs indicative of networkperformance (and/or other data usable for corrective decisions) and toforward the results to the provisioning module 207 and/or to thedatabase 204.

Those skilled in the art will readily appreciate that the presentlydisclosed subject matter is not bound by the configuration of FIG. 2;equivalent and/or modified functionality can be consolidated or dividedin another manner and can be implemented in any appropriate combinationof software with firmware and hardware. In some embodiments, NI module101 can be implemented as one or more standalone entities operating inconjunction with the SON system, or can be integrated, fully or partly,with other network entities (e.g. OSS, etc.). Optionally, at least partof functionality of NI module 101 can be implemented in a cloud and/ordistributed arrangement (e.g. functions of collecting UE data, providingpolicy updates, etc.).

UE data and/or UE KPIs obtained by SON system 121 are usable for avariety of use cases.

By way of non-limiting example. UE data are usable for uplink/downlinkbalancing and, thereby, solving the problem of “access black hole”. Asschematically illustrated with reference to FIG. 3a , due toconfiguration and/or physical properties of AP 304, its uplink. coverage302 can be less than downlink coverage 301-1. Unavailable uplinktogether with downlink matching to operational signal strengthrequirements lead to a connectivity problem unrecognizable by thenetwork, and is referred to as an “access black hole”.

For example, as defined by respective standards, LTE provides UE with aseparate PRACH channel (Physical Random Access Channel) necessary toperform Random Access Procedure. Such a procedure is required forinitial access from idle state, connection re-establishment procedure,etc. Accordingly, an idle UE located in “black hole” area 303 isincapable to access AP 304, whilst NW data related to AP 304 comprise noindication of such a connection problem. Accordingly, a user in a “blackhole” area (typically at the edge of coverage of AP 304) is incapable ofinitiating a voice and/or data service.

The “access black hole” is the use case solvable with the help of theSON system configured in accordance with certain embodiments of thepresently disclosed subject matter. The respective operation of SONsystem 121 is illustrated with reference to FIG. 4.

SON system (e.g. NI module 101) processes collected. UE data to obtain(401) UE KPI(s) indicative of failed connectivity attempts (referred tohereinafter as “connectivity UE KPIs”). It is noted that UE data arecollected in association with data indicative of geolocation ofrespective UEs when they obtained the respective data. Optionally, NImodule 101 can process only part of the collected UE data (e.g.corresponding to certain geographical areas and/or certainAPs/parameters thereof and/or certain UEs). By way of non-limitingexample. NI module can, prior to processing (401), identify edges ofrespective APs' coverage (e.g. corresponding to UEs reporting RSRP <11.0dbm) and process only data collected from the respective locations. Itis noted that data indicative of failed connectivity attempts can bereported when respective UEs leave the “access black hole area”.Optionally, UE data indicative of failed connectivity attempts can bereceived with the help of the UE agent configured to provide activeconnectivity tests (e.g. by sending ICMP (ping) requests).

Processing (401) can comprise clustering the collected data (e.g. perlocation bins) and separately processing each cluster and/or groupthereof. It is noted that each processed cluster (or group thereof)shall comprise a statistically sufficient amount of data enablingdesired statistical confidence (e.g. 70-99% depending on therequirements of the network operator). By way of non-limiting example,the statistically sufficient amount of data can be achieved by tuningthe duration of the collection period, aggregating the collected UE datatogether with prior obtained UE data stored in database 204, using thestored UE data to generate a statistical baseline, etc.

Alternatively or additionally, UE KPI(s) indicative of failedconnectivity attempts can be received by SON system 121 from an externalsource.

SON system (e.g. NI module 101) uses the obtained UE KPIs to identify(402) location bins characterized by UE “connectivity KPIs” that do notmatch a predefined threshold (e.g. UE KPIs indicative of the number ofaccess failures exceeding a predefined value). Further, SON system (e.g.NI module 101) identifies (403) AP(s) corresponding to the identifiedproblematic location bins. By way of non-limiting example, correlationbetween location bins and APs can be provided using data informative ofnetwork topology and/or using UE data associated both withserving/neighbouring APs and UE locations.

Among the identified APs, SON system identifies (404) APs with the“access black hole” problem, i.e. characterized by areas withunavailable uplink whilst having downlink that matches operationalsignal strength requirements.

In certain embodiments such AP(s) can be identified using the obtainedUE KPIs together with NW KPIs so to identify AP(s) matching both of thefollowing criteria:

-   -   1) APs corresponding to identified location bins;    -   2) APs characterized by NW KPIs indicative that respective        downlinks match operational signal strength requirements.

In another embodiments, AP(s) with the “access black hole” problem canbe identified by processing merely the collected UE data. In suchembodiments, the UE agent can be configured to cause UEs to store and,responsive to a predefined event triggering UE data reporting, to reportdata indicative of received signal strength. By way of non-limitingexample, such an event can be a successful connection attempt, scheduledreporting event, or any other suitable event configured by the policy.Accordingly, the SON system uses such data to identify (404) APs with“access black hole” as APs corresponding to identified location bins,whilst enabling received signal strength corresponding to normaldownlink operation.

Upon identifying APs with “access black holes”, the SON system enablescorrective actions to reduce (405) downlink covering the identifiedlocation bins by identified APs (e.g. by gradually adjusting parametersof the identified APs, e.g. antenna tilt and/or minimum signal strengthfor UE during cell selection (for example, QRXLEVmin). Optionally, theSON system can further enable adjusting (406) parameters of neighbouringAPs, if necessary, for providing desired coverage of the “access blackhole” location.

Following the adjustment, NI module 101 processes updated collected UEdata to obtain (407) updated UE KPIs (and, optionally, updated NW KPIs)characterizing the identified APs.

NI module 101 repeats operations 403-405 until a completion criteria ismet. Optionally, NI module 101 can repeat operations 402-405 untilmeeting the completion criteria. By way of non-limiting example, thecompletion criteria for a given A.P can be met when the updated KPI(s)meet respectively predefined thresholds. FIG. 3b illustrates AP 304 withdownlink (301-2) coverage adjusted as above (covering the “access blackhole” area by neighbouring APs is not shown for simplicity ofillustration). It is noted that operations 401-405 can be provided innear real-time mode. It is further noted that NW KPI(s) can be used as afeedback before beginning a next cycle of operations 403-405 (or402-405).

By way of another non-limiting example, UE KPIs are usable for improvingquality of services for commuting users. As the number of stationaryusers is, typically, a significantly larger number than the number ofcommuting users served by a certain AP, averaged KPI-based APoptimization is focused towards the stationary users, and the problemsexperienced by commuting users can be overlooked.

As illustrated in FIG. 5, SON system (e.g. NI module 101) processescollected UE data to identify (501) UEs associated with currentlycommuting users and obtains (502) UE KPIs of respective serving APs withrespect to the identified commuting UEs. By way of non-limiting example,such UEs can be identified in accordance with reported location (e.g. iflocation accuracy is enough to distinguish between a highway andsurrounding area), combination of reported location and speed derivedfrom the reported UE data, data of 3^(rd) party applications (e.g. Waze,Google Maps, etc.) derived from the reported UE data, etc. Likewise inthe other use cases, NI module 101 can process only part of thecollected UE data (e.g. corresponding to certain geographical areasand/or certain AP/parameters thereof and/or certain UEs, etc.).

SON system 121 uses the obtained UE KPIs to identify (503) APs requiringcorrective actions in view of insufficient quality of service (e.g.insufficient reliability, retainability, latency, etc.) provided forcommuting UEs. For the identified APs, SON system 121 enables (504)corrective actions (e.g. gradually adjusting AP parameters) to improveAPs' performance with regard to commuting users whilst maintainingadequate averaged AP performance. Thus, the corrective actions can beprovided so as to improve UE KPIs related to the identified APs, whilstkeeping respective NW KPIs as matching one or more predefinedthresholds.

Current averaged performance of a given AP can be defined with the helpof NW KPI(s) characterizing the given AP and/or with the help of UE KPisderived from UE data (including stationary UEs) associated with thegiven AP. Following the adjustment, NI module 101 processes (505) theupdated aggregated NW data to obtain updated NW KPIs characterizing theadjusted identified AP(s) and processes updated UE data to obtainupdated UE KPIs with regard to commuting UEs. Alternatively, NI module101 can obtain enhanced KPIs further detailed with reference to FIG. 7,each enhanced KPI informative of both updated NW KPIs and updated UEKPIs. UE KPI within the enhanced KPI can be weighted to emphasise theimpact of commuting users' experience on the AP performance metrics.

SON system 121 repeats operations 504-506 until a completion criteria ismet. By way of non-limiting example, the completion criteria can be metwhen both updated NW KPI(s) and updated UE KPI(s) with regard tocommuting UEs meet respectively predefined thresholds.

It is noted that operations 501-506 can be provided in near real-timemode.

Among advantages enabled for the use case disclosed with reference toFIG. 5 is capability of significant improvement of service for commutingusers, whilst affordable (and, in many cases, unnoticeable) degradationof service for static users.

Referring to FIG. 6, there is illustrated a generalized flow chart ofoperating the SON system, in accordance with certain embodiments of thepresently disclosed subject matter, SON system (e.g. NI module)continuously obtains (601) data informative of NW KPIs and continuouslyobtains (602) data informative of UE KPIs. Such data can be receivedfrom different entities (e.g. external crowdsourcing applicationscollecting the respective data). Alternatively or additionally, SONsystem can obtain at least part of KPI-informative data by separateprocessing NW and UE data collected therein. Collecting NW and UE datais further detailed with reference to FIG. 7.

SON system processes (603) the obtained UE KPIs (optionally, togetherwith NW KPIs) to identify (604) APs that require corrective action and,optionally, to identify (605) desirable corrective actions. In certainembodiments, desirable corrective actions can be the corrective actionsthat enable improving the one or more UE KPIs related to the identifiedAPs, whilst keeping respective NW KPIs as matching one or morepredefined thresholds.

Further, SON system can report (606) such identified APs (e.g. to OSS orother appropriate network entities), identify problems with identifiedAPs, and alert (607) about identified problems, enable the identifieddesirable corrective actions (608) and/or can provide other reporting oractions related to the required correction of the identified APs.

In certain embodiments, aggregation criteria of statistical clustersusable for deriving NW KPIs can differ from aggregation criteria ofstatistical clusters usable for deriving UE KPIs. By way of non-limitingexample, NW KPI can be derived from NW data aggregated in statisticalclusters per cell (or groups thereof), while UE KPIs can be derived fromUE data aggregated in statistical clusters per geographical areas (e.g.location bins and/or groups thereof). For example, a geographical areacan correspond to, at least, part of a highway or railway or to anotherterritory of interest (e.g. enterprise, convention centre, etc.).Alternatively or additionally, an aggregation criterion can be relatedto a speed of UEs moving (e.g. statistical clusters can include only UEslocated in a geographical area corresponding to a highway/railway andmoving with a speed exceeding a predefined threshold).

In such embodiments processing (603) can comprise correlation between NWKPIs and UE KPIs prior to processing the respective KPIs. Correlation,for example, can include identifying NW KPIs that correspond to APsinvolved in services characterized by UE KPIs. By way of non-limitingexample, such correlation can be provided using data informative ofnetwork topology and/or UE data associated both withserving/neighbouring APs and UE locations. Optionally, data instatistical clusters for NW KPIs and/or UE KPIs can be furtheraggregated by type of UE, time of day, services, predefined use cases(e.g. uplink/downlink balancing, hotspots identification, LTE footprintoptimization, etc.), etc.

Alternatively or additionally, NW KPIs and UE KPIs can be indicative ofdifferent characteristics related to the network performance. By way ofnon-limiting example, while NW KPIs can be indicative of networkperformance parameters (e.g. number of call drops, latency, etc.),respective UE KPIs can be indicative of a user's experience (e.g. numberof UEs which suffered from call drops, number of UEs with a certainservice which suffered from high latency, etc.).

Referring to FIG. 7, there is illustrated a generalized flow chart ofoperating the SON system in accordance with another embodiments of thepresently disclosed subject matter.

NI module 101 continuously obtains (701) NW data informative of networkperformance. At least part of NW data can be derived by the NI module byaggregating and periodically parsing data collected from the one or moreND depositories 102.

NI module 101 continuously collects (702) UE data informative, at least,of network performance and UE location. In accordance with certainembodiments of the presently disclosed subject matter, a given UE of theplurality of UEs can send UE data to the NI module responsive to one ormore triggering events specified by a policy received from NI module101. By way of non-limiting example, triggering events related to agiven UE can include changes of network connectivity, starts/ends ofphone calls, connection to a car docking station, start/end ofpredefined period, etc. Depending on the triggering event, UE cancollect data during a predefined period (e.g. responsive to a call, UEcan collect UE data during the call; responsive to a start of periodicalcollection, UE can collect UE data during a predefined period, etc.)) orcan report current state of the UE device (e.g. in response toconnection or signal strength changes, etc.). Alternatively oradditionally, the policy can specify that the given UE collects and/orreports data only when served by predefined APs and/or located in apredefined area. Alternatively or additionally, the policy can specifythat the given UE (e.g. with the help of the UE agent) provides someactive measurements (e.g. active connectivity tests by sending ICMP pingrequests), and reports the respective results.

NI module 101 aggregates (703) the NW and UE data obtained during acollection period. In certain embodiments NW data and UE data can beaggregated in separate statistical clusters. Aggregation criteria can bethe same or, optionally, aggregation criteria for NW data can differfrom aggregation criteria for UE data. By way of non-limiting example,NW data can be aggregated in statistical clusters per APs (or groupsthereof), while UE data can be aggregated in statistical clusters pergeographical areas (e.g. location bins and/or groups thereof).

In another embodiments, NW data and UE data can be aggregated incombined statistical clusters. Optionally, UE data can be aggregatedwith different weights depending on respective UE location and/or othercontext-related data. By way of non-limiting example, weights can dependon clients' priorities defined by a network operator (e.g. enterpriseclients, youngsters, VIP clients, etc.), on a time of day (e.g. workinghours), on UE location e.g. in a train or on a highway), UE status e.g.speed of moving, indoor/outdoor, etc.), applications running on UEduring UE data collection, etc. Aggregating NW and UE data in a combinedcluster (e.g. per geolocation) can comprise correlation between NW dataassociated with APs and UE data associated with both APs andgeolocations.

Alternatively or additionally, data for statistical clusters can beaggregated by type of UE, time of day, services, predefined use cases(e.g. uplink/downlink balancing, hotspots identification, LTE footprintoptimization, etc.), etc. It is noted that each statistical clustershall comprise a statistically sufficient amount of data enablingdesired statistical confidence. By way of non-limiting example, thisrequirement can be to achieved by tuning the duration of the collectionperiod, aggregating the collected data together with prior obtained NWand/or UE data stored in database 204, using the stored NW and UE datato generate a statistical baseline, etc.

NI module 101 processes the aggregated data to obtain (704) keyperformance indicators generated in consideration of the obtained UEdata, such indicators being referred to hereinafter as “enhanced KPIs”.

When NW data have been aggregated separately from UE data, generatingenhanced KPIs can comprise correlation between NW data clusters and UEdata clusters prior to further processing thereof. By way ofnon-limiting example, such correlation can be provided using datainformative of network topology and/or UE data associated both withserving/neighbouring APs and UE locations.

At least part of the enhanced KPIs can be configured as a single valuerepresenting results of processing statistical clusters with combined NWand UE data. Alternatively or additionally, at least part of theenhanced. KPIs can be configured as a single value representing amathematical (e.g. weighted) combination of KPIs derived from NW dataand KPIs separately derived from UE data. Alternatively or additionally,a given enhanced KPI can be configured as a combination of two values,respectively corresponding to NW KPI and UE KPI. Optionally, for thesame collected data, NI module 101 can generate several enhanced KPIscorresponding to different use cases (e.g. with different weights for UEdata in combined statistical clusters or different weights of UE KPIsseparately derived from UE data).

It is noted that the use cases detailed with reference to FIGS. 4 and 5can be implemented with independently applicable NW KPIs and UE KPIs asdetailed with reference to FIG. 6. Likewise, these use cases can beimplemented with enhanced KPIs detailed with reference to FIG. 7.

SON system 121 can report (706) the enhanced KPIs to predefined networkentities. Alternatively or additionally, SON system 121 can compare thegenerated enhanced KPIs with predefined thresholds. The thresholds forthe same enhanced KPI can differ for different use cases and/orgeographical areas. SON system 121 can alert respective network entities(e.g. OSS and/or administrator's work station) when one or more enhancedKPIs do not match the thresholds and, accordingly, are indicative ofperformance problem(s).

Optionally, SON system 121 further uses the obtained enhanced KPIs toidentify (605) APs requiring corrective actions. SON system 121 canreport (707) the identified APs requiring corrective actions torespective network entities (e.g. OSS). In certain embodiments, SONsystem 121 further identifies (708) the desirable corrective actions,generates provisioning scripts (or select pre-provisioned scripts) andenables the corrective actions accordingly.

It is noted that in the methods detailed with reference to FIGS. 4-7,the respective corrective actions can be defined with the help ofrule-base model, machine learning, neural networks or any otherappropriate technique using NW KPIs and UE KPIs as inputs, separately orin a combination. The collected data can be usable for training modelsfor machine learning or artificial intelligence techniques. Likewise,these techniques can use UE KPIs and/or enhanced KIPs as feedback forthe training process.

It is to be understood that the presently disclosed subject matter isnot limited in its application to the details set forth in thedescription contained herein or illustrated in the drawings. Thepresently disclosed subject matter is capable of other embodiments andof being practiced and carried out in various ways.

It will also be understood that the presently disclosed subject matterfurther contemplates a non-transitory machine-readable memory tangiblyembodying a program of instructions executable by the machine forexecuting the method of the invention.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of theinvention as hereinbefore described without departing from its scope,defined in and by the appended claims.

The invention claimed is:
 1. A method of controlling traffic in acellular network comprising a plurality of access points (APs) serving aplurality of user equipment devices (UEs), the method comprising: by acomputerized system operatively connected to the plurality of APs andthe plurality of UE devices, continuously obtaining network data (NWdata) related to at least part of APs from the plurality of APs;continuously obtaining, from the UE devices, user equipment data (UEdata) related to at least part of the UEs from the plurality of UEs;aggregating the NW data and the UE data into statistical clusters,wherein the NW data are aggregated into one or more NW statisticalclusters using a first aggregation criterion and the UE data areseparately aggregated into one or more UE statistical clusters using asecond aggregation criterion different from the first aggregationcriterion; or the NW data and the UE data are aggregated into one ormore combined statistical clusters, wherein UE data are aggregated withweights different from the weights of aggregating the NW data; andcontinuously identifying at least one AP requiring corrective action byprocessing data informative of Network Key Performance Indicators (NWKPIs) and User Equipment Key Performance Indicators (UE KPIs), the dataderived by processing the statistical clusters informative of NW dataand UE data.
 2. The method of claim 1 further comprising at least oneof: a. reporting the at least one identified AP requiring correctiveactions to one or more predefined network entities; b. identifyingproblems related to the at least one identified AP and alerting thereofto one or more predefined network entities; c. identifying one or moredesirable corrective actions with regard to the at least one identifiedAP; and d. identifying desirable corrective actions with regard to theat least one identified AP and enabling thereof.
 3. The method of claim1, wherein the processing comprises deriving one or more NW KPIs fromthe one or more NW statistical clusters, deriving one or more UE KPIsfrom the one or more UE statistical clusters, correlating between theone or more NW KPIs and the one or more UE KPIs and processing thecorrelated KPIs to identify the at least one AP requiring correctiveaction, wherein the correlating comprises identifying NW KPIs thatcorrespond to APs involved in one or more services characterized by theone or more UE KPIs.
 4. The method of claim 1, further comprisingenabling, with regard to the identified at least one AP, one or morecorrective actions, wherein the one or more corrective actions improveone or more UE KPIs related to the identified at least one AP and keepdeterioration of one or more NW KPIs related to the identified at leastone AP as matching one or more predefined thresholds.
 5. The method ofclaim 1, wherein the second aggregation criterion is related to UEslocated in one or more predefined geographical areas and/or UEs movingwith a speed exceeding a predefined threshold.
 6. The method of claim 3,wherein the one or more NW KPIs and the one or more UE KPIs areindicative of different characteristics related to network performance.7. The method of claim 3, wherein the one or more NW KPIs are indicativeof network performance parameters, and the one or more UE KPIs areindicative of number of UEs respectively suffered from insufficientnetwork performance.
 8. The method of claim 1, wherein the processingcomprises: correlating between the one or more NW statistical clustersand the one or more UE statistical clusters, wherein the correlating isprovided using, at least: data informative of network topology and/or UEdata associated both with UE locations and APs serving the respectiveUEs, and/or UE data associated both with UE locations and APsneighboring APs serving the respective UEs; deriving one or more NW KPIsfrom the one or more NW statistical clusters; deriving one or more UEKPIs from the one or more UE statistical clusters; generating one ormore enhanced KPIs, wherein at least one enhanced KPI is configured as asingle value representing a weighted combination of the one or more NWKPIs and one or more UE KPIs derived from respectively correlatedstatistical clusters; and using the one or more enhanced KPIs toidentify the at least one AP requiring corrective action.
 9. The methodof claim 8, wherein a weight of the one or more UE KPIs in the at leastone enhanced KPI depends on a use case related to desirable correctiveactions, the use case selected from: uplink/downlink balancing, hotspotsidentification, LTE footprint optimization.
 10. The method of claim 1,wherein the processing comprises: correlating between the one or more NWstatistical clusters and the one or more UE statistical clusters,wherein the correlating is provided using, at least, data informative ofnetwork topology and/or UE data associated both with UE locations andAPs serving the respective UEs; deriving one or more NW KPIs from the NWstatistical clusters; deriving one or more UE KPIs from the UEstatistical clusters; and generating one or more enhanced KPIs, whereinat least one enhanced KPI is configured as two values, one of the twovalues corresponding to the one or more NW KPIs and another of the twovalues, to respectively correlated one or more UE KPIs; and using theone or more enhanced KPIs to identify the at least one AP requiringcorrective action.
 11. The method of claim 1, wherein the processingcomprises generating one or more enhanced KPIs informative of both NWKPIs and UE KPIs and configured as a single value derived from thecombined statistical clusters; and using the one or more enhanced KPIsto identify the at least one AP requiring corrective action.
 12. Themethod of claim 1, wherein the weight of UE data when aggregated in thecombined statistical clusters depends on a use case selected from:uplink/downlink balancing, hotspots identification, LTE footprintoptimization.
 13. A computerized system operatively connected to aplurality of APs operating in a cellular network and to a plurality ofUE devices served by the plurality of APs, the computerized systemcomprising a computer configured to: continuously obtain network data(NW data) related to at least part of APs from the plurality of APs;continuously obtain, from the UE devices, user equipment data (UE data)related to at least part of the UEs from the plurality of UEs; aggregatethe NW data and the UE data into statistical clusters, wherein the NWdata are aggregated into one or more NW statistical clusters using afirst aggregation criterion and the UE data are separately aggregatedinto one or more UE statistical clusters using a second aggregationcriterion different from the first aggregation criterion; or the NW dataand the UE data are aggregated into one or more combined statisticalclusters, wherein UE data are aggregated with weights different from theweights of aggregating the NW data; and continuously identify at leastone AP requiring corrective action by processing data informative ofNetwork Key Performance Indicators (NW KPIs) and User Equipment KeyPerformance Indicators (UE KPIs), the data derived by processing thestatistical clusters informative of NW data and UE data.
 14. Thecomputerized system of claim 13, wherein the processing comprisesderiving one or more NW KPIs from the one or more NW statisticalclusters, deriving one or more UE KPIs from the one or more UEstatistical clusters, correlating between the one or more NW KPIs andthe one or more UE KPIs and processing the correlated KPIs to identifythe at least one AP requiring corrective action, wherein the correlatingcomprises identifying NW KPIs that correspond to APs involved in one ormore services characterized by the one or more UE KPIs.
 15. Thecomputerized system of claim 13, further configured to enable, withregard to the identified at least one AP, one or more correctiveactions, wherein the one or more corrective actions improve one or moreUE KPIs related to the identified at least one AP and keep deteriorationof one or more NW KPIs related to the identified at least one AP asmatching one or more predefined thresholds.
 16. The computerized systemof claim 13, wherein the processing comprises: correlating between theone or more NW statistical clusters and the oneor more UE statisticalclusters, wherein the correlating is provided using, at least: datainformative of network topology and/or UE data associated both with UElocations and APs serving the respective UEs, and/or UE data associatedboth with UE locations and APs neighboring APs serving the respectiveUEs; deriving one or more NW KPIs from the one or more NW statisticalclusters; deriving one or more UE KPIs from the one or more UEstatistical clusters; generating one or more enhanced KPIs, wherein atleast one enhanced KPI is configured as a single value representing aweighted combination of the one or more NW KP Is and one or more UE KPIsderived from respectively correlated statistical clusters; and using theone or more enhanced KPIs to identify the at least one AP requiringcorrective action.
 17. The computerized system of claim 13, wherein theprocessing comprises: correlating between the one or more NW statisticalclusters and the one or more UE statistical clusters, wherein thecorrelating is provided using, at least, data informative of networktopology and/or UE data associated both with UE locations and APsserving the respective UEs; deriving one or more NW KPIs from the NWstatistical clusters; deriving one or more UE KPIs from the UEstatistical clusters; and generating one or more enhanced KPIs, whereinat least one enhanced KPI is configured as two values, one of the twovalues corresponding to the one or more NW KPIs and another of the twovalues, to respectively correlated one or more UE KPIs; and using theone or more enhanced KPIs to identify the at least one AP requiringcorrective action.
 18. The computerized system of claim 13, wherein theprocessing comprises generating one or more enhanced KPIs informative ofboth NW KPIs and UE KPIs and configured as a single value derived fromthe combined statistical clusters; and using the one or more enhancedKPIs to identify the at least one AP requiring corrective action. 19.The computerized system of claim 13, wherein the weight of UE data whenaggregated in the combined statistical clusters depends on a use caseselected from: uplink/downlink balancing, hotspots identification, LTEfootprint optimization.
 20. A non-transitory computer readable mediumusable by a computerized system operatively connected to a plurality ofAPs operating in a cellular network and to a plurality of UE devicesserved by the plurality of APs, the computer readable medium comprisinginstructions that, when executed by a computer, cause the computer toperform operations comprising: aggregating into statistical clusterscontinuously obtained network data (NW data) related to at least part ofAPs from the plurality of APs and continuously obtained, from the UEdevices, user equipment data (UE data) related to at least part of theUEs from the plurality of UEs, wherein the NW data are aggregated intoone or more NW statistical clusters using a first aggregation criterionand the UE data are separately aggregated into one or more UEstatistical clusters using a second aggregation criterion different fromthe first aggregation criterion; or the NW data and the UE data areaggregated into one or more combined statistical clusters, wherein UEdata are aggregated with weights different from the weights ofaggregating the NW data; and continuously identifying at least one APrequiring corrective action by processing data informative of NetworkKey Performance Indicators (NW KPIs) and User Equipment Key PerformanceIndicators (UE KPIs), the data derived by processing the statisticalclusters informative of NW data and UE data.